import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	str         =    {"""tokenizer_file""": """tokenizer.json"""}

__a    :	Tuple         =    {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					List[str]     					=		VOCAB_FILES_NAMES
			__a    :					Dict     					=		PRETRAINED_VOCAB_FILES_MAP
			__a    :					Optional[int]     					=		['''input_ids''', '''attention_mask''']
			__a    :					Optional[int]     					=		None





			def __init__(  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__="<unk>" ,  lowerCAmelCase__="<s>" ,  lowerCAmelCase__="</s>" ,  lowerCAmelCase__="<pad>" ,  lowerCAmelCase__=False ,  lowerCAmelCase__=False ,  **lowerCAmelCase__ ,  )	->   int:

							'''simple docstring'''


							super().__init__(
							    lowerCAmelCase__ ,  lowerCAmelCase__ ,  tokenizer_file=lowerCAmelCase__ ,  unk_token=lowerCAmelCase__ ,  bos_token=lowerCAmelCase__ ,  eos_token=lowerCAmelCase__ ,  pad_token=lowerCAmelCase__ ,  add_prefix_space=lowerCAmelCase__ ,  clean_up_tokenization_spaces=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )
							__lowercase					     =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
							if pre_tok_state.get('''add_prefix_space''' ,  lowerCAmelCase__		) != add_prefix_space:
											__lowercase					     =				getattr(lowerCAmelCase__ ,  pre_tok_state.pop('''type'''		)		)
											__lowercase					     =				add_prefix_space
											__lowercase					     =				pre_tok_class(**lowerCAmelCase__		)

							__lowercase					     =				add_prefix_space





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   BatchEncoding:

							'''simple docstring'''


							__lowercase					     =				kwargs.get('''is_split_into_words''' ,  lowerCAmelCase__		)
							if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
											    ''' pretokenized inputs.'''		)

							return super()._batch_encode_plus(*lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   BatchEncoding:

							'''simple docstring'''


							__lowercase					     =				kwargs.get('''is_split_into_words''' ,  lowerCAmelCase__		)

							if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
											    ''' pretokenized inputs.'''		)

							return super()._encode_plus(*lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   Tuple[str]:

							'''simple docstring'''


							__lowercase					     =				self._tokenizer.model.save(lowerCAmelCase__ ,  name=lowerCAmelCase__		)
							return tuple(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				[]
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCAmelCase__ ,  add_special_tokens=lowerCAmelCase__		) + [self.eos_token_id]		)

							if len(lowerCAmelCase__		) > self.model_max_length:
											__lowercase					     =				input_ids[-self.model_max_length :]
							return input_ids
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings